SoroushVahidi/scidocs huggingface dataset:
An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns
(2009)

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.
Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms
(2017)

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
k-Nearest Neighbor Augmented Neural Networks for Text Classification
(2017)

In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.
Active intelligent antenna system for car2car
(2013)

In this paper an innovative antenna system for car2car applications is described. The antenna system is based on an active antenna module containing the antennas as well as the complete car2car chip hardware and software. This module is very small, so that it can be integrated e.g. into a shark fin antenna on the cars rooftop. Moreover additional modules can be mounted somewhere else in the car and connected with the main module via Ethernet. This allows high flexibility and re-configurability of the system. Two test cars have been equipped with the new car2car demonstrator systems and measurements have been performed in the 3D measurement chamber as well as in dynamic outdoor scenarios. The new system shows excellent performance in measurements and field trials.
GenomeGraphs: integrated genomic data visualization with R
(2008)

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.
The effect of construal level on subjective probability estimates.
(2009)

In a series of studies, we examined novel predictions drawn from a conceptualization of probability as psychological distance. Manipulating construal level in a number of different ways and examining a variety of probability judgments, we found that participants led to adopt a high-level-construal mind-set made lower probability assessments than did those led to adopt a low-level-construal mind-set. Moreover, this occurred even when construal level was manipulated in a context separate from the judgment task and the manipulation was unrelated in content to the events being judged. These findings suggest that broad processing variables can exert a widespread influence on probability judgment.
Auto-directed video stabilization with robust L1 optimal camera paths
(2011)

We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.
Learning Graphical State Transitions
(2017)

Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
(2016)

We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing “keywords” (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded “understanding” of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (humancreated) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the endto-end task of image captioning.
Scheduling a Major College Basketball Conference - Revisited
(2001)

Nemhauser and Trick presented the problem of finding a timetable for the 1997/98 Atlantic Coast Conference (ACC) in basketball. Their solution, found with a combination of integer programming and exhaustive enumeration, was accepted by the ACC. Finite-domain constraint programming is another programming technique that can be used for solving combinatorial search problems such as sports tournament scheduling. This paper presents a solution of round robin tournament planning based on finite-domain constraint programming. The approach yields a dramatic performance improvement, which makes an integrated interactive software solution feasible.
Collection, exploration and analysis of crowdfunding social networks
(2016)

Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.
Exploiting Emoticons in Polarity Classification of Text
(2015)

With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level – and, to a lesser extent, on a sentence level – significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text.
Full-Bridge+SRT Hybrid DC/DC Converter for a 6.6-kW EV On-Board Charger
(2016)

This paper suggests a hybrid pulsewidth-modulation (PWM) dc/dc converter for an electric vehicle (EV) on-board charger (OBC). It is composed of a full-bridge (FB) converter and a resonant converter with secondary resonance tank (SRT). They are coupled in a single transformer so that any power distribution problem that causes a size increase in the transformer can be solved. Dual power flow of the proposed charger provides an increase in charger gain and a reduction of turn-off voltage spikes of the rectifying diodes caused by transformer leakage inductance and diode junction capacitance. These characteristics help reduce diode loss and remove dissipative snubber circuitry. In addition, this structure has the additional advantage of output inductor size due to the reduction of voltage variation applied to the inductor. The feasibility of the proposed charger is verified with a 6.6-kW prototype implemented by a design procedure.
Pronoun Translation and Prediction with or without Coreference Links
(2015)

The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronounfocused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: grammatical constraints from the hypothesized coreference links, and candidate translations from an SMT decoder. The system for the second sub-task avoids hypothesizing a coreference link, and uses instead a large set of source-side and target-side features from the noun phrases surrounding the pronoun to train a pronoun predictor.
Learning Simpler Language Models with the Delta Recurrent Neural Network Framework
(2017)

Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and highperforming design that unifies previously proposed gated neural models. The DeltaRNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. In language modeling at the word and character levels, the models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, perform comparably to several state-of-the-art baselines. At the subword level, we find the Delta-RNN’s performance to be comparable to that of complex gated architectures.
Modified reduced switch symmetrical multilevel inverter
(2017)

Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.
Searchable Encryption over Feature-Rich Data
(2018)

Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.
Practical byzantine fault tolerance and proactive recovery
(2002)

Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.
Blockchain-based secure firmware update for embedded devices in an Internet of Things environment
(2016)

Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device’s firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.
ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER
(2014)

The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.
How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions]
(2017)

^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body’s electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon’s powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don’t-touch world that Pabellon envisions.